The existing GNN-based methods [1]}, [2]} that learn the node representation over dynamic graphs can be considered discrete dynamical systems.
Chen et al. NODE demonstrate that the continuous dynamical systems are more efficient for modeling continuous-time dynamic data.
The discrete networks can roughly be regarded as continuous networks by stacking enough layers. However, Onno and Suzuki 2019Graph point out that graph neural networks (GNNs) exponentially lose expressive power for downstream tasks, which will lead to over-smoothing problems as we add more hidden layers.
Therefore, designing effective continuous Graph Neural Networks to model continuous-time dynamics of node representation on dynamic graphs is critical.
To this end, many continuous graph neural networks [3]}, [4]} have been proposed recently. Although those mentioned above continuous dynamic neural networks are more efficient to model the graph data, few approaches have been proposed for dealing with dynamic graphs using continuous-time dynamic neural networks.
